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analysis and professional skills that are necessary for bioscience research and related non-academic careers. https://www.yorkshirebiosciencedtp.ac.uk Project Description: This PhD project explores a cutting
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, contribute to a better world. We look forward to receiving your application! We invite applications for a fully funded PhD student position to join the research group of Jan Glaubitz to work on Bayesian
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invited for an interview in English. The successful applicant is expected to begin in spring 2026 (start date is flexible). You will enroll in the PhD program (4 years) at the Faculty of Science, University
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manage externally and internally funded research projects, including in co-operation with DNV Participate in and initiate research projects in co-operation with industry, government, and national as
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celebrate diversity and consider equality and inclusion as fundamental aspects of all our activities. Application procedure The application should be written in English be attached as PDF-files, as below
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the Paris region specializing in environmental and climate sciences at the Institut Pierre-Simon Laplace (IPSL) (https://www.ipsl.fr ). The LOCEAN team's work focuses on studying the physical processes
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, from viruses to vertebrates. PhD: 3-4 years full-time; 5 years part-time; MSc (Research): 1 year full-time; 2 years part-time; Apply now Research projects Research projects Evolution of ecological and
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of Text Analytics and head of the GATE team part of the NLP group at The University of Sheffield. She is a world-leading researcher in Natural Language Processing. She has been a leading developer of the
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application process here. About the position The Department of Materials Science and Engineering (IMA) at the Natural Science Faculty, has a vacancy for a position as PhD candidate related to modelling
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
PhD candidate to develop and apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular